The Interplay between Dengue Virus and the Human Innate Immune System: A Game of Hide and Seek
Bibliographic record
Abstract
With 40% of the world population at risk, infections with dengue virus (DENV) constitute a serious threat to public health. While there is no antiviral therapy available against this potentially lethal disease, the efficacy of the only approved vaccine is not optimal and its safety has been recently questioned. In order to develop better vaccines based on attenuated and/or chimeric viruses, one must consider how the human immune system is engaged during DENV infection. The activation of the innate immunity through the detection of viruses by cellular sensors is the first line of defence against those pathogens. This triggers a cascade of events which establishes an antiviral state at the cell level and leads to a global immunological response. However, DENV has evolved to interfere with the innate immune signalling at multiple levels, hence dampening antiviral responses and favouring viral replication and dissemination. This review elaborates on the interplay between DENV and the innate immune system. A special focus is given on the viral countermeasure mechanisms reported over the last decade which should be taken into consideration during vaccine development.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".